mirror of
https://github.com/HKUDS/LightRAG.git
synced 2025-06-26 22:00:19 +00:00
46 lines
1.1 KiB
Python
46 lines
1.1 KiB
Python
import os
|
|
|
|
from lightrag import LightRAG, QueryParam
|
|
from lightrag.llm import ollama_model_complete, ollama_embedding
|
|
from lightrag.utils import EmbeddingFunc
|
|
|
|
WORKING_DIR = "./dickens"
|
|
|
|
if not os.path.exists(WORKING_DIR):
|
|
os.mkdir(WORKING_DIR)
|
|
|
|
rag = LightRAG(
|
|
working_dir=WORKING_DIR,
|
|
llm_model_func=ollama_model_complete,
|
|
llm_model_name="your_model_name",
|
|
embedding_func=EmbeddingFunc(
|
|
embedding_dim=768,
|
|
max_token_size=8192,
|
|
func=lambda texts: ollama_embedding(texts, embed_model="nomic-embed-text"),
|
|
),
|
|
)
|
|
|
|
|
|
with open("./book.txt") as f:
|
|
rag.insert(f.read())
|
|
|
|
# Perform naive search
|
|
print(
|
|
rag.query("What are the top themes in this story?", param=QueryParam(mode="naive"))
|
|
)
|
|
|
|
# Perform local search
|
|
print(
|
|
rag.query("What are the top themes in this story?", param=QueryParam(mode="local"))
|
|
)
|
|
|
|
# Perform global search
|
|
print(
|
|
rag.query("What are the top themes in this story?", param=QueryParam(mode="global"))
|
|
)
|
|
|
|
# Perform hybrid search
|
|
print(
|
|
rag.query("What are the top themes in this story?", param=QueryParam(mode="hybrid"))
|
|
)
|